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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import smart_cond
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import googletest
def raise_exception():
raise RuntimeError("did not expect to be called")
class SmartCondTest(test_util.TensorFlowTestCase):
def testTrue(self):
with ops.Graph().as_default():
with session.Session():
x = constant_op.constant(2)
y = constant_op.constant(5)
z = smart_cond.smart_cond(True, lambda: math_ops.multiply(x, 16),
lambda: math_ops.multiply(y, 5))
self.assertEqual(z.eval(), 32)
def testFalse(self):
with ops.Graph().as_default():
with session.Session():
x = constant_op.constant(4)
y = constant_op.constant(3)
z = smart_cond.smart_cond(False, lambda: math_ops.multiply(x, 16),
lambda: math_ops.multiply(y, 3))
self.assertEqual(z.eval(), 9)
def testUnknown(self):
with ops.Graph().as_default():
with session.Session():
x = array_ops.placeholder(dtype=dtypes.int32)
y = smart_cond.smart_cond(x > 0, lambda: constant_op.constant(1),
lambda: constant_op.constant(2))
self.assertEqual(y.eval(feed_dict={x: 1}), 1)
self.assertEqual(y.eval(feed_dict={x: -1}), 2)
def testEval(self):
with ops.Graph().as_default():
with session.Session():
x = constant_op.constant(1)
y = constant_op.constant(2)
# x * y > 0 can be evaluated at graph construction time, so the false
# branch shouldn't be evaluated at all.
z = smart_cond.smart_cond(x * y > 0, lambda: constant_op.constant(1),
raise_exception)
self.assertEqual(z.eval(feed_dict={x: 1}), 1)
def testPlaceholderWithDefault(self):
with ops.Graph().as_default():
with session.Session():
x = array_ops.placeholder_with_default(1, shape=())
y = smart_cond.smart_cond(x > 0, lambda: constant_op.constant(1),
lambda: constant_op.constant(2))
self.assertEqual(y.eval(), 1)
self.assertEqual(y.eval(feed_dict={x: -1}), 2)
def testMissingArg1(self):
with ops.Graph().as_default():
with session.Session():
x = constant_op.constant(1)
with self.assertRaises(TypeError):
smart_cond.smart_cond(True, false_fn=lambda: x)
def testMissingArg2(self):
with ops.Graph().as_default():
with session.Session():
x = constant_op.constant(1)
with self.assertRaises(TypeError):
smart_cond.smart_cond(True, lambda: x)
class SmartCaseTest(test_util.TensorFlowTestCase):
def testTrue(self):
x = array_ops.placeholder(dtype=dtypes.int32, shape=[])
conditions = [(True, lambda: constant_op.constant(1)),
(x == 0, raise_exception)]
y = smart_cond.smart_case(conditions, default=raise_exception,
exclusive=False)
z = smart_cond.smart_case(conditions, default=raise_exception,
exclusive=True)
with session.Session() as sess:
# No feed_dict necessary
self.assertEqual(sess.run(y), 1)
self.assertEqual(sess.run(z), 1)
def testFalse(self):
conditions = [(False, raise_exception)]
y = smart_cond.smart_case(conditions,
default=lambda: constant_op.constant(1),
exclusive=False)
z = smart_cond.smart_case(conditions,
default=lambda: constant_op.constant(1),
exclusive=True)
with session.Session() as sess:
self.assertEqual(sess.run(y), 1)
self.assertEqual(sess.run(z), 1)
def testMix(self):
x = array_ops.placeholder(dtype=dtypes.int32, shape=[])
y = constant_op.constant(10)
conditions = [(x > 1, lambda: constant_op.constant(1)),
(y < 1, raise_exception),
(False, raise_exception),
(True, lambda: constant_op.constant(3))]
z = smart_cond.smart_case(conditions, default=raise_exception)
with session.Session() as sess:
self.assertEqual(sess.run(z, feed_dict={x: 2}), 1)
self.assertEqual(sess.run(z, feed_dict={x: 0}), 3)
class SmartConstantValueTest(test_util.TensorFlowTestCase):
# TODO(skyewm): this is essentially a regression test for
# TF_TryEvaluateConstant, and is not really a valid smart_constant_value test
# (smart_constant_value is only supposed to return bools). Move the
# TF_TryEvaluateConstant call to tensor_util.constant_value and make this a
# constant_value test instead.
def testCond(self):
with ops.Graph().as_default():
pred = array_ops.placeholder_with_default(True, shape=())
x = control_flow_ops.cond(pred,
lambda: constant_op.constant(1),
lambda: constant_op.constant(2))
self.assertIsNone(smart_cond.smart_constant_value(x))
if __name__ == "__main__":
googletest.main()
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